contrast_of_interest = 'P_simple_STIM_stimlin_high_gt_low'
contrast_of_interest = 'P_simple_STIM_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 3.306831 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6858907 Bit rate: 22.71 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
%disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 5 participants, size is now 67
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…" "participants that are outliers:... su…"
disp(n);
{'sub-0051'} {'sub-0074'} {'sub-0084'} {'sub-0093'} {'sub-0129'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain 0.0314 4.7419 0.0000 1.0000 ***
Cog Wholebrain 0.0183 4.8386 0.0000 1.0000 ***
Emo Wholebrain -0.0461 -8.0049 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} 0.031372 0.0066058 4.7492 1.0314e-05 0.5597
{'Cog Wholebrain' } 0.018276 0.0037771 4.8387 7.3651e-06 0.57024
{'Emo Wholebrain' } -0.046062 0.005743 -8.0206 1.5316e-11 -0.94524
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [10.0024 11.0024 12.0024]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} 0.030659 0.0061205 5.0092 3.8476e-06 0.59034
{'Cog Wholebrain' } 0.016462 0.0033724 4.8815 6.261e-06 0.5753
{'Emo Wholebrain' } -0.044541 0.005265 -8.4598 2.3476e-12 -0.99699
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [13.0024 14.0024 17.0013]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'V_simple_STIM_stimlin_high_gt_low'
contrast_of_interest = 'V_simple_STIM_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 3.270726 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6889156 Bit rate: 22.72 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 69
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0051" "participants that are outliers:... sub-0084" "participants that are outliers:... sub-0098"
disp(n);
{'sub-0051'} {'sub-0084'} {'sub-0098'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0083 -1.1620 0.2491 0.0000
Cog Wholebrain -0.0050 -0.9940 0.3236 0.0000
Emo Wholebrain 0.0124 1.6172 0.1103 0.0000
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ ________ _______ ________
{'Pain Wholebrain'} -0.0083493 0.0071956 -1.1603 0.2498 -0.13675
{'Cog Wholebrain' } -0.004986 0.005013 -0.99461 0.3233 -0.11722
{'Emo Wholebrain' } 0.012368 0.007648 1.6172 0.11028 0.19058
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [10.0026 11.0026 12.0026]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ ________ ________
{'Pain Wholebrain'} -0.0093508 0.0072392 -1.2917 0.20066 -0.15223
{'Cog Wholebrain' } -0.0049508 0.004798 -1.0319 0.30564 -0.12161
{'Emo Wholebrain' } 0.01352 0.0076287 1.7723 0.080642 0.20886
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [13.0026 14.0026 17.0015]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'C_simple_STIM_stimlin_high_gt_low'
contrast_of_interest = 'C_simple_STIM_stimlin_high_gt_low'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 3.292915 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6891280 Bit rate: 22.72 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 3 participants, size is now 69
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0014" "participants that are outliers:... sub-0082" "participants that are outliers:... sub-0120"
disp(n);
{'sub-0014'} {'sub-0082'} {'sub-0120'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0079 -1.4500 0.1515 0.0000
Cog Wholebrain -0.0214 -4.5034 0.0000 1.0000 ***
Emo Wholebrain 0.0267 4.4589 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.007895 0.0054386 -1.4517 0.151 -0.17108
{'Cog Wholebrain' } -0.021446 0.00476 -4.5054 2.5449e-05 -0.53097
{'Emo Wholebrain' } 0.02668 0.0059818 4.4602 3.0012e-05 0.52564
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [10.0027 11.0027 12.0027]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.0071976 0.0053391 -1.3481 0.18191 -0.15888
{'Cog Wholebrain' } -0.02087 0.0046224 -4.5149 2.4575e-05 -0.53209
{'Emo Wholebrain' } 0.026123 0.005877 4.445 3.1723e-05 0.52384
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [13.0027 14.0027 17.0016]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
contrast_of_interest = 'motor'
contrast_of_interest = 'motor'
mount_dir = '/Volumes/spacetop_projects_cue/analysis/fmri/spm/univariate/model01_6cond_highlowcue_rampplateau/1stlevel';
'P_VC_STIM_cue_high_gt_low', 'V_PC_STIM_cue_high_gt_low', 'C_PV_STIM_cue_high_gt_low',...% contratss
'P_VC_STIM_stimlin_high_gt_low', 'V_PC_STIM_stimlin_high_gt_low', 'C_PV_STIM_stimlin_high_gt_low',...
'P_VC_STIM_stimquad_med_gt_other', 'V_PC_STIM_stimquad_med_gt_other', 'C_PV_STIM_stimquad_med_gt_other',...
'P_VC_STIM_cue_int_stimlin','V_PC_STIM_cue_int_stimlin', 'C_PV_STIM_cue_int_stimlin',...
'P_VC_STIM_cue_int_stimquad','V_PC_STIM_cue_int_stimquad','C_PV_STIM_cue_int_stimquad',...
'P_simple_STIM_cue_high_gt_low', 'V_simple_STIM_cue_high_gt_low', 'C_simple_STIM_cue_high_gt_low',... % dummay contrasts
'P_simple_STIM_stimlin_high_gt_low', 'V_simple_STIM_stimlin_high_gt_low', 'C_simple_STIM_stimlin_high_gt_low',...
'P_simple_STIM_stimquad_med_gt_other','V_simple_STIM_stimquad_med_gt_other', 'C_simple_STIM_stimquad_med_gt_other',...
'P_simple_STIM_cue_int_stimlin', 'V_simple_STIM_cue_int_stimlin', 'C_simple_STIM_cue_int_stimlin',...
'P_simple_STIM_cue_int_stimquad', 'V_simple_STIM_cue_int_stimquad','C_simple_STIM_cue_int_stimquad',...
'P_simple_STIM_highcue_highstim', 'P_simple_STIM_highcue_medstim', 'P_simple_STIM_highcue_lowstim',... % pain events
'P_simple_STIM_lowcue_highstim', 'P_simple_STIM_lowcue_medstim', 'P_simple_STIM_lowcue_lowstim',...
'V_simple_STIM_highcue_highstim', 'V_simple_STIM_highcue_medstim', 'V_simple_STIM_highcue_lowstim',... % vicarious events
'V_simple_STIM_lowcue_highstim', 'V_simple_STIM_lowcue_medstim', 'V_simple_STIM_lowcue_lowstim',...
'C_simple_STIM_highcue_highstim', 'C_simple_STIM_highcue_medstim', 'C_simple_STIM_highcue_lowstim',... % cognitive events
'C_simple_STIM_lowcue_highstim', 'C_simple_STIM_lowcue_medstim', 'C_simple_STIM_lowcue_lowstim',...
'P_VC_CUE_cue_high_gt_low','V_PC_CUE_cue_high_gt_low','C_PV_CUE_cue_high_gt_low',...% cue epoch contrasts
'P_simple_CUE_cue_high_gt_low','V_simple_CUE_STIM_cue_high_gt_low','C_simple_CUE_cue_high_gt_low',...% cue epoch dummy
'G_simple_CUE_cue_high_gt_low',...
'P_VC_STIM', 'V_PC_STIM', 'C_PV_STIM'
index = find(strcmp(contrast_name, contrast_of_interest));
con_name = sprintf('*con_%04d.nii', index);
con_list = dir(fullfile(mount_dir, '*', con_name));
con_fldr = {con_list.folder}; fname = {con_list.name};
con_files = strcat(con_fldr,'/', fname)';
con_data_obj = fmri_data(con_files);
Using default mask: /Users/h/Documents/MATLAB/CanlabCore/CanlabCore/canlab_canonical_brains/Canonical_brains_surfaces/brainmask_canlab.nii
Direct calls to spm_defauts are deprecated.
Please use spm('Defaults',modality) or spm_get_defaults instead.
loading mask. mapping volumes.
checking that dimensions and voxel sizes of volumes are the same.
Pre-allocating data array. Needed: 28753056 bytes
Loading image number: 72
Elapsed time is 3.244472 seconds.
Image names entered, but fullpath attribute is empty. Getting path info.
Number of unique values in dataset: 6888883 Bit rate: 22.72 bits
disp(strcat("current length is ", num2str(size(con_data_obj.dat,2))));
%for s = 1:length(wh_outlier_corr)
% disp(strcat("-------subject", num2str(s), "------"))
con.dat = con_data_obj.dat(:,~wh_outlier_corr);
con.image_names = con_data_obj.image_names(~wh_outlier_corr,:);
con.fullpath = con_data_obj.fullpath(~wh_outlier_corr,:);
con.files_exist = con_data_obj.files_exist(~wh_outlier_corr,:);
disp(strcat("after removing ", num2str(sum(wh_outlier_corr)), " participants, size is now ",num2str(size(con.dat,2))));
after removing 2 participants, size is now 70
[path,n,e] = fileparts(con_fldr(wh_outlier_corr));
disp(strcat("participants that are outliers:... ", n));
"participants that are outliers:... sub-0044" "participants that are outliers:... sub-0088"
disp(n);
{'sub-0044'} {'sub-0088'}
[obj, names] = load_image_set('pain_cog_emo');
Loaded images:
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Pain.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Cognitive_Control.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_aMCC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_pACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_sgACC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_vmPFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_dMFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_MFC_Negative_Emotion.nii
/Users/h/Documents/MATLAB/Neuroimaging_Pattern_Masks/Multivariate_signature_patterns/2018_Kragel_MFC_Generalizability/bPLS_Wholebrain_Negative_Emotion.nii
bpls_wholebrain = get_wh_image(obj, [8 16 24]);
names_wholebrain = names([8 16 24]);
create_figure('Kragel Pain-Cog-Emo maps', 1, 3);
stats = image_similarity_plot(con_data_obj, 'average', 'mapset', bpls_wholebrain, 'networknames', names_wholebrain, 'nofigure');
Table of correlations Group:1
--------------------------------------
T-test on Fisher's r to Z transformed point-biserial correlations
R_avg T P sig
Pain Wholebrain -0.0082 -1.8276 0.0718 0.0000
Cog Wholebrain 0.0299 11.6792 0.0000 1.0000 ***
Emo Wholebrain -0.0190 -4.3232 0.0000 1.0000 ***
barplot_columns(stats.r', 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.008244 0.0045121 -1.8271 0.071892 -0.21532
{'Cog Wholebrain' } 0.029943 0.002562 11.687 2.2204e-15 1.3774
{'Emo Wholebrain' } -0.019015 0.0043979 -4.3236 4.9163e-05 -0.50955
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [10.0028 11.0028 12.0028]
ylabel('Pattern similarity (r)');
title('Similarity (r) with patterns')
test_data_obj = resample_space(con_data_obj, bpls_wholebrain);
csim(:, i) = canlab_pattern_similarity(test_data_obj.dat, bpls_wholebrain.dat(:, i), 'cosine_similarity');
end
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
Warning: Some images have zero values in some of the 411578 voxels in weight mask. These will be excluded from similarity analysis image-wise.
Number of zero or NaN values within weight mask, by input image:
83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83 83
barplot_columns(csim, 'nofigure', 'colors', {[1 .9 0] [.2 .2 1] [1 .2 .2]}, 'names', names_wholebrain)
Col 1: Pain Wholebrain Col 2: Cog Wholebrain Col 3: Emo Wholebrain
---------------------------------------------
Tests of column means against zero
---------------------------------------------
Name Mean_Value Std_Error T P Cohens_d
___________________ __________ _________ _______ __________ ________
{'Pain Wholebrain'} -0.0041255 0.004078 -1.0117 0.31514 -0.11923
{'Cog Wholebrain' } 0.026864 0.002331 11.524 2.2204e-15 1.3582
{'Emo Wholebrain' } -0.020797 0.0040213 -5.1717 2.0547e-06 -0.60949
ans =
fig_han: [1×1 struct]
axis_han: [1×1 Axes]
bar_han1: [1×1 Bar]
bar_han: {[1×1 Bar] [1×1 Bar] [1×1 Bar]}
errorbar_han: {[1×1 ErrorBar] [1×1 ErrorBar] [1×1 ErrorBar]}
point_han1: {72×3 cell}
text_han: {72×3 cell}
point_han: {72×3 cell}
star_handles: [13.0028 14.0028 17.0017]
ylabel('Pattern similarity (cosine sim)');
title('Pattern response (cosine similarity)')
% pubfilename = '6cond_cueeffect_contrast.mlx';
% p = struct('useNewFigure', false, 'maxHeight', 800, 'maxWidth', 800, ...
% 'format', 'html', 'outputDir', pubdir, ...
% 'showCode', true, 'stylesheet', which('mxdom2simplehtml_CANlab.xsl'));
% htmlfile = publish(pubfilename, p);